论文标题
无人机基站轨迹优化基于污染后搜索和救援操作中的强化学习
UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations
论文作者
论文摘要
由于灾难，地面基站（TBS）将部分崩溃。某些用户设备（UE）将是无关的。将无人驾驶汽车（UAV）部署为航空站是一种快速覆盖UES的方法。但是现有的方法仅是指无人机的覆盖范围。在这种情况下，他们专注于在灾后区域的部署无人机，所有TBS不再工作。关于可用的TBS和无人机组合的研究有限。我们提出了部署与可用TBS作为空中基站的无人机配合的方法。并通过增强学习来改善覆盖范围。此外，在实验中，我们首先使用层次结构（桦木）以平衡的迭代减少和聚类进行聚类。最后，通过Q学习实现基站对UES的更好覆盖。
Because of disaster, terrestrial base stations (TBS) would be partly crashed. Some user equipments (UE) would be unserved. Deploying unmanned aerial vehicles (UAV) as aerial base stations is a method to cover UEs quickly. But existing methods solely refer to the coverage of UAVs. In those scenarios, they focus on the deployment of UAVs in the post-disaster area where all TBSs do not work any longer. There is limited research about the combination of available TBSs and UAVs. We propose the method to deploy UAVs cooperating with available TBSs as aerial base stations. And improve the coverage by reinforcement learning. Besides, in the experiments, we cluster UEs with balanced iterative reducing and clustering using hierarchies (BIRCH) at first. Finally, achieve base stations' better coverage to UEs through Q-learning.
